Salman, N, Ghogho, M orcid.org/0000-0002-0055-7867 and Kemp, AH (2014) Optimized Low Complexity Sensor Node Positioning in Wireless Sensor Networks. IEEE Sensors Journal, 14 (1). pp. 39-46. ISSN 1530-437X
Abstract
Localization of sensor nodes in wireless sensor networks (WSNs) promotes many new applications. A longer life time is imperative for WSNs, this requirement constrains the energy consumption and computation power of the nodes. To locate sensors at a low cost, the received signal strength (RSS)-based localization is favored by many researchers. RSS positioning does not require any additional hardware on the sensors and does not consume extra power. A low complexity solution to RSS localization is the linear least squares (LLS) method. In this paper, we analyze and improve the performance of this technique. First, a weighted least squares (WLS) algorithm is proposed, which considerably improves the location estimation accuracy. Second, reference anchor optimization using a technique based on the minimization of the theoretical mean square error is also proposed to further improve performance of LLS and WLS algorithms. Finally, to realistically bound the performance of any unbiased RSS location estimator based on the linear model, the linear Cramer-Rao bound (CRB) is derived. It is shown via simulations that employment of the optimal reference anchor selection technique considerably improves system performance, while the WLS algorithm pushes the estimation performance closer to the linear CRB. Finally, it is also shown that the linear CRB has larger error than the exact CRB, which is the expected outcome.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Keywords: | Localization; Received signal strength (RSS); estimation theory; least squares approximations; mean square error methods; minimisation; wireless sensor networks; CRB; LLS method; WLS algorithm; WSN; energy consumption; linear Cramer-Rao bound model; linear least square method; minimization technique; node power computation; optimal reference anchor selection technique; optimized low complexity sensor node; positioning; received signal strength; reference anchor optimization; theoretical mean square error; unbiased RSS location estimator; weighted least square algorithm; wireless sensor network; Cramer–Rao bound; Complexity theory; Covariance matrices; Maximum likelihood estimation; Noise; Vectors; Wireless sensor networks |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jan 2015 14:38 |
Last Modified: | 27 Feb 2019 16:29 |
Published Version: | http://dx.doi.org/10.1109/JSEN.2013.2278864 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/JSEN.2013.2278864 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:82376 |